Context Aware Machine Learning Approaches for Modeling Elastic Localization in Three-Dimensional Composite Microstructures

Ruoqian Liu1, Yuksel C. Yabansu2, Zijiang Yang1, Alok N. Choudhary1, Surya R. Kalidindi2,3, Ankit Agrawal1
1Department of Electrical Engineering and Computer Science, Northwestern University, Evanston, USA
2George W. Woodruff School of Mechanical Engineering, Georgia Institute of Technology, Atlanta, USA
3School of Computational Science and Engineering, Georgia Institute of Technology, Atlanta, USA

Tóm tắt

The response of a composite material is the result of a complex interplay between the prevailing mechanics and the heterogenous structure at disparate spatial and temporal scales. Understanding and capturing the multiscale phenomena is critical for materials modeling and can be pursued both by physical simulation-based modeling as well as data-driven machine learning-based modeling. In this work, we build machine learning-based data models as surrogate models for approximating the microscale elastic response as a function of the material microstructure (also called the elastic localization linkage). In building these surrogate models, we particularly focus on understanding the role of contexts, as a link to the higher scale information that most evidently influences and determines the microscale response. As a result of context modeling, we find that machine learning systems with context awareness not only outperform previous best results, but also extend the parallelism of model training so as to maximize the computational efficiency.

Tài liệu tham khảo

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